Using a newly developed data set, we analyze the effects of infrastructure investment on economic performance in Portugal. A vector-autoregressive approach estimates the elasticity and marginal products of twelve types of infrastructure investment on private investment, employment, and output. We find that the largest long-term accumulated effects come from investments in railroads, ports, airports, health, education, and telecommunications. For these infrastructures, the output multipliers suggest that these investments pay for themselves through additional tax revenues. For investments in ports, airports and education infrastructures, the bulk of the effects are short-term demand-side effects, while for railroads, health, and telecommunications, the impact is mostly of a long-term and supply-side nature. Finally, investments in health and airports exhibit decreasing marginal returns, with railroads, ports, and telecommunications being relatively stable. In terms of the other infrastructure assets, the economic effects of investments in municipal roads, electricity and gas, and refineries are insignificant, while investments in national roads, highways, and waste and waste water have positive economic effects but too small to improve the public budget. Clearly, from a policy perspective, not all infrastructure investments in Portugal are created equal.

Marine geological maps of the Campania region have been constructed both to a 1:25.000 and to a 1:10.000 scale in the frame of the research projects financed by the Italian National Geological Survey, focusing, in particular, on the Gulf of Naples (Southern Tyrrhenian Sea), a complex volcanic area where volcanic and sedimentary processes strongly interacted during the Late Quaternary and on the Cilento Promontory offshore. In this paper, the examples of the geological sheets n. 464 “Isola di Ischia” and n. 502 “Agropoli” have been studied. The integration of the geological maps with the seismo-stratigraphic setting of the study areas has also been performed based on the realization of interpreted seismic profiles, providing interesting data on the geological setting of the subsurface. The coastal geological sedimentation in the Ischia and Agropoli offshore has been studied in detail. The mapped geological units are represented by: i) the rocky units of the acoustic basement (volcanic and/or sedimentary); ii) the deposits of the littoral environment, including the deposits of submerged beach and the deposits of toe of coastal cliff; iii) the deposits of the inner shelf environment, including the inner shelf deposits and the bioclastic deposits; iv) the deposits of the outer shelf environment, including the clastic deposits and the bioclastic deposits; v) the lowstand system tract; vi) the Pleistocene relict marine units; vii) different volcanic units in Pleistocene age. The seismo-stratigraphic data, coupled with the sedimentological and environmental data provided by the geological maps, provided us with new insights on the geologic evolution of this area during the Late Quaternary.

This study adopts a discursive and analytical perspective to explore how technological advances are reconfiguring the dynamics of the global labour market, with special attention to the phenomenon of microwork. Microwork, characterised by short, fragmented tasks carried out through digital platforms and geographically distributed, has seen exponential growth, particularly in nations with lower economic development. This type of work shows a growing distinction between tasks of a complex and creative nature and those of a repetitive and monotonous nature that do not require advanced skills to perform. This differentiation can intensify wage disparities between developed and developing countries, as well as contribute to the precariousness of work in activities considered less complex and valued. The article highlights the emergence of unstable and poorly paid jobs that do not require specific qualifications and discusses their impact on social security systems in countries where labour regulations are insufficient. Using a theoretical-methodological approach, the research examines the role of artificial intelligence in the rise of micro-labour and its socio-economic implications. It concludes that despite the flexibility and short-term earning opportunities offered by microwork, it poses considerable challenges in terms of income security, workers’ rights, and social protection, emphasising the need for regulatory measures to mitigate its adverse effects on vulnerable communities.

Cysteine is one of the body’s essential amino acids to build proteins. For the early diagnosis of a number of diseases and biological issues, L-cysteine (L-Cys) is essential. Our study presents an electrochemical sensor that detects L-cysteine by immobilizing the horseradish peroxidase (HRP) enzyme on a reduced graphene oxide (GCE) modified glassy carbon electrode. The morphologies and chemical compositions of synthesized materials were examined using Fourier transform infrared spectroscopy (FTIR) and field-emission scanning electron microscopy (FESEM). The modified electrode’s electrochemical behavior was investigated using cyclic voltammetry (CV). Cyclic voltammetry demonstrated HRP/rGO/GCE has better electrocatalytic activity than bare GCE in the oxidation of L-cysteine oxidation in a solution of acetate buffer. The electrochemical sensor had a broad linear range of 0 µM to 1 mM, a 0.32 µM detection limit, and a sensitivity of 6.08 μA μM⁻¹ cm⁻². The developed sensor was successfully used for the L-cysteine detection in a real blood sample with good results.

The number of domestic studies on "variational pragmatics" (Ren Yuxin, Chen Xinren, 2012) is very limited. The research scopes are also relatively limited, which has not yet attracted the attention of more researchers. Therefore, based on this book The Routledge Handbook of Second Language Acquisition and Pragmatics, this paper aims to sort out and summarize the development trend of pragmatics from the meaning, goal and theory of variational pragmatics, and then put forward suggestions for future research.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.